Open AccessContent‐based image quality assessment using semantic information and luminance differences
Author(s) -
Qi Huan,
Jiao Shuhong,
Lin Weisi,
Tang Lin,
Shen Weihe
Publication year - 2014
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2014.1651
Subject(s) - luminance , metric (unit) , distortion (music) , image quality , computer science , artificial intelligence , computer vision , enhanced data rates for gsm evolution , quality (philosophy) , image (mathematics) , information retrieval , pattern recognition (psychology) , engineering , physics , amplifier , computer network , operations management , bandwidth (computing) , quantum mechanics
A full‐reference image quality assessment (FR‐IQA) metric, with emphasis on semantic information changes in different image content areas, is presented. The changes on edge information, that can represent semantic information changes, are calculated based on the characteristics of different image content areas. Considering that edge changes cannot account for luminance changes while luminance changes does affect visual quality of images, the luminance changes are also incorporated into the design of the perceptual quality metric. Experimental results confirm that the proposed metric is consistent with human judgments of quality, and outperforms relevant state‐of‐the‐art metrics across various distortion types.